Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 178
Filter
1.
J Nurs Manag ; 2022 Oct 19.
Article in English | MEDLINE | ID: covidwho-2078582

ABSTRACT

AIMS: To investigate the relationships between transformational leadership, psychological empowerment, and nurses' innovative behavior. BACKGROUND: The innovative behavior of nurses is important to adapt to the changing medical environment. However, there is currently a limited understanding of the relationship between nurses' innovative behavior and transformational leadership and psychological empowerment during the pandemic. METHODS: Convenience sampling was used to conduct an investigation involving 1317 nurses from ten hospitals in China from January 2022 to April 2022. Data analysis was performed using correlation analysis, univariate analysis and multiple regression analysis. The STROBE checklist was followed when writing this manuscript. RESULTS: High transformational leadership and high psychological empowerment were associated with high innovative behavior. The results of the multiple linear regression analysis showed that physical condition, whether or not you have attended academic conferences, whether or not you have participated in fund research projects, transformational leadership, and psychological empowerment were the main factors on nurses' innovative behavior, together explaining 64.5% of the total variance. CONCLUSION: Promotion of transformational leadership and psychological empowerment is vital for nurses to promote innovation, thereby meeting the urgent demand for innovative nurses and the rapid development of nursing disciplines. Implications for Nursing Management This study highlights the importance of transformational leadership in developing nurses' innovative behaviors. Understanding the role of psychological empowerment can help nurse managers formulate relevant intervention strategies and cultivate nurses' innovative behavior.

2.
Frontiers in immunology ; 13, 2022.
Article in English | EuropePMC | ID: covidwho-2073977

ABSTRACT

Background COVID-19 has caused a global pandemic and the death toll is increasing. With the coronavirus continuously mutating, Omicron has replaced Delta as the most widely reported variant in the world. Studies have shown that the plasma of some vaccinated people does not neutralize the Omicron variant. However, further studies are needed to determine whether plasma neutralizes Omicron after one- or two-dose vaccine in patients who have recovered from infection with the original strain. Methods The pseudovirus neutralization assays were performed on 64 plasma samples of convalescent COVID-19 patients, which were divided into pre-vaccination group, one-dose vaccinated group and two-dose vaccinated group. Results In the three groups, there were significant reductions of sera neutralizing activity from WT to Delta variant (B.1.617.2), and from WT to Omicron variant (B.1.1.529) (ps<0.001), but the difference between Delta and Omicron variants were not significant (p>0.05). The average neutralization of the Omicron variant showed a significant difference between pre-vaccination and two-dose vaccinated convalescent individuals (p<0.01). Conclusions Among the 64 plasma samples of COVID-19 convalescents, whether vaccinated or not, Omicron (B.1.1.529) escaped the neutralizing antibodies, with a significantly decreased neutralization activity compared to WT. And two-dose of vaccine could significantly raise the average neutralization of Omicron in convalescent individuals.

3.
Eur J Radiol Open ; 9: 100438, 2022.
Article in English | MEDLINE | ID: covidwho-2061087

ABSTRACT

Objectives: When diagnosing Coronavirus disease 2019(COVID-19), radiologists cannot make an accurate judgments because the image characteristics of COVID-19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods: We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results: We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94-0.98), sensitivity 0.92 (95 % CI, 0.88-0.94), pooled specificity 0.91 (95 % CI, 0.87-0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions: The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

4.
Eur J Pediatr ; 181(12): 4011-4017, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2035054

ABSTRACT

During the coronavirus disease 2019 (COVID-19) epidemic, many reports have indicated that children shed the virus longer than adults in stool, and that most of the children had mild or even asymptomatic infections, which increased the potential risk for feces to be a source of contamination and may play an important role in the spread of the virus. In this review, we collected relevant literature to summarize the duration of fecal viral shedding in children with COVID-19. We found that in about 60% of the cases, the fecal shedding time was between 28 and 42 days, which was much longer than that of adults. We further explored the possible reason for prolonged shedding and its the potential impact. The poor hand hygiene practices of children, their tendency to swallow sputum and/or saliva, the significant difference in expression of angiotensin-converting enzyme 2 (ACE2) in intestine between children and adults, and the variance in immune status and intestinal microbiome could be considered as potential casual agents of longer fecal viral shedding duration of children.   Conclusion: Children with COVID-19 show prolonged fecal shedding compared to adults. Several mechanisms may be involved in the longer fecal viral shedding. Viral shedding in the stool could be contributing to a possible route of transmission. Therefore, we think that further preventive measures in children should be taken to reduce the spread of the disease. What is Known: • Children with COVID-19 are more likely to have asymptomatic infections and to experience mild symptoms. • Some patients continue to shed the virus in feces, despite respiratory samples testing negative. What is New: • Children with COVID-19 carried a longer-term fecal viral shedding than adults. • The poor hand hygiene practices of children, their tendency to swallow sputum and/or saliva, the difference in expression of ACE2 in intestine between children and adults, and the variance in immune status and intestinal microbiome could be considered as potential casual agents of longer fecal viral shedding duration of children.


Subject(s)
COVID-19 , Child , Adult , Humans , Virus Shedding , Angiotensin-Converting Enzyme 2 , SARS-CoV-2 , Asymptomatic Infections , RNA, Viral , Feces
5.
Medicine ; 101(37), 2022.
Article in English | EuropePMC | ID: covidwho-2034113

ABSTRACT

Acute respiratory tract infections pose a serious threat to the health of children worldwide, with viral infections representing a major etiology of this type of disease. Protective measures such as mask-wearing, social distancing, and hand hygiene can be effective in curbing the spread of severe acute respiratory syndrome coronavirus 2. These precautions may also have an impact on the spread of other respiratory viruses. In this study, we retrospectively compared the respiratory virus infections of children in Southwest China before and after the outbreak of COVID-19. Nasopharyngeal swabs were collected from 1578 patients under 14 years old with acute respiratory tract infection symptoms before and after COVID-19 pandemic. Nine common respiratory viruses including human bocavirus, human rhinoviruses, human coronaviruses, human adenoviruses, human metapneumovirus, respiratory syncytial virus, influenza A virus, influenza B virus, and parainfluenza virus were measured by advanced fragment analysis. The respiratory virus infection rates among children of all ages and genders in Southwest China under the precautions against COVID-19 pandemic were significantly lower than that of the same period before the pandemic. Our findings indicate that public health measures implemented during the COVID-19 pandemic, including strict mask-wearing, social distancing, and hand hygiene, may be effective in preventing the transmission of other respiratory viruses in children, thereby controlling the spread of infections.

6.
Front Psychiatry ; 13: 872331, 2022.
Article in English | MEDLINE | ID: covidwho-2032821

ABSTRACT

Background: The sporadic coronavirus disease (COVID-19) epidemic has placed enormous psychological stress on people, especially clinicians. The objective of this study was to examine depression, anxiety, quality of life (QOL), and related social psychological factors among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China and to provide a reference for formulating reasonable countermeasures. Methods: In this cross-sectional study, demographic information, COVID-19-related questions, anxiety (Generalized Anxiety Disorder-7, GAD-7), depression (Patient Health Questionnaire-9, PHQ-9), insomnia (Insomnia Severity Index, ISI), stress (Perceived Stress Scale-10, PSS-10), and QOL (World Health Organization Quality of Life-brief version, WHOQOL-BREF) were collected. Binary logistic regression analysis was used to test the relationships between anxiety and/or depression and other related problems. Multiple linear regression analysis was used to test the relationships among factors influencing QOL. Results: A total of 146 young front-line clinicians were included. The prevalence rates of depression, anxiety, and anxiety-depression comorbidity were 37.7% (95% CI = 29.7-45.6%), 26.0% (95% CI = 18.8-33.2%), and 24.0% (95% CI = 17.0-31.0%), respectively. Severe stress (OR = 1.258, 95% CI = 1.098-1.442, P < 0.01) and insomnia (OR = 1.282, 95% CI = 1.135-1.447, P < 0.01) were positively correlated with depression. Severe stress (OR = 1.487, 95% CI = 1.213-1.823, P < 0.01) and insomnia (OR = 1.131, 95% CI = 1.003-1.274, P < 0.05) were positively correlated with anxiety. Severe stress (OR = 1.532, 95% CI = 1.228-1.912, P < 0.01) was positively correlated with anxiety-depression comorbidity. However, insomnia (OR = 1.081, 95% CI = 0.963-1.214, P > 0.05) was not correlated with anxiety-depression comorbidity. The belief that the vaccine will stop the COVID-19 pandemic (OR = 0.099, 95% CI = 0.014-0.715, P < 0.05) was negatively correlated with anxiety and anxiety-depression comorbidity (OR = 0.101, 95% CI = 0.014-0.744, P < 0.05). Severe stress (B = -0.068, 95% CI = -0.129 to -0.007, P < 0.05) and insomnia (B = -0.127, 95% CI = -0.188 to -0.067, P < 0.01) were negatively correlated with QOL. The belief that the vaccine could provide protection (B = 1.442, 95% CI = 0.253-2.631, P < 0.05) was positively correlated with QOL. Conclusions: The prevalence of depression, anxiety, and even anxiety-depression comorbidity was high among young front-line clinicians in high-risk areas during the COVID-19 sporadic epidemic in China. Various biological and psychological factors as well as COVID-19-related factors were associated with mental health issues and QOL. Psychological intervention should evaluate these related factors and formulate measures for these high-risk groups.

8.
Arch Med Sci ; 18(5): 1262-1270, 2022.
Article in English | MEDLINE | ID: covidwho-2025069

ABSTRACT

Introduction: Coronavirus disease 2019 (COVID-19) is associated with severe emotional changes. This research aims to investigate the prevalence of anxiety and depression in COVID-19 patients and its relationship with disease severity, sleep patterns, lifestyle, and specific laboratory test results. Material and methods: An observational study of 52 Chinese patients with COVID-19 was conducted to assess the relation between anxiety and depression (evaluated with the Hospital Anxiety and Depression Scale) and laboratory findings (lymphocytes, C-reactive proteins, leukocytes, alanine aminotransferase, aspartate aminotransferase). The relationships between the severity of COVID-19 in patients, the Insomnia Severity Index (ISI) score, and the Hospital Anxiety and Depression Scale (HADS) score were also investigated. Results: There were statistically significant associations between disease, smoking, and HADS-A scores (p = 0.011/0.020). The HADS-D score of patients with the disease was higher than in those without a past medical history (p = 0.008). The difference in C-reactive protein (CRP) between different lung infections, the HADS-A and HADS-D scores between different ages and ISI groups, and the correlation between the two scores were statistically significant. Conclusions: Anxiety and depression are associated with poor sleep quality, smoking, and past medical history in patients with COVID-19. Additionally, anxiety and depression were seen to coexist, and there was a positive correlation between them. Further, the inflammatory index CRP was significantly increased in bilateral lung infections.

9.
Front Psychiatry ; 13: 963419, 2022.
Article in English | MEDLINE | ID: covidwho-2022915

ABSTRACT

Background: A better understanding of the factors and their correlation with clinical first-line nurses' sleep, fatigue and mental workload is of great significance to personnel scheduling strategies and rapid responses to anti-pandemic tasks in the post-COVID-19 pandemic era. Objective: This multicenter and cross-sectional study aimed to investigate the nurses' sleep, fatigue and mental workload and contributing factors to each, and to determine the correlation among them. Methods: A total of 1,004 eligible nurses (46 males, 958 females) from three tertiary hospitals participated in this cluster sampling survey. The Questionnaire Star online tool was used to collect the sociodemographic and study target data: Sleep quality, fatigue, and mental workload. Multi-statistical methods were used for data analysis using SPSS 25.0 and Amos 21.0. Results: The average sleep quality score was 10.545 ± 3.399 (insomnia prevalence: 80.2%); the average fatigue score was 55.81 ± 10.405 (fatigue prevalence: 100%); and the weighted mental workload score was 56.772 ± 17.26. Poor sleep was associated with mental workload (r = 0.303, P < 0.05) and fatigue (r = 0.727, P < 0.01). Fatigue was associated with mental workload (r = 0.321, P < 0.05). COVID-19 has caused both fatigue and mental workload. As 49% of nurses claimed their mental workload has been severely affected by COVID-19, while it has done slight harm to 68.9% of nurses' sleep quality. Conclusion: In the post-COVID-19 pandemic era, the high prevalence of sleep disorders and fatigue emphasizes the importance of paying enough attention to the mental health of nurses in first-class tertiary hospitals. Efficient nursing strategies should focus on the interaction of sleep, fatigue and mental workload in clinical nurses. In that case, further research on solutions to the phenomenon stated above proves to be of great significance and necessity. Clinical trial registration: [https://clinicaltrials.gov/], identifier [ChiCTR2100053133].

10.
Front Psychol ; 13: 781274, 2022.
Article in English | MEDLINE | ID: covidwho-2022856

ABSTRACT

Background: Negative life events in middle school students have a significant impact on depression. However, the mechanism of this association is not fully understood. This study used rumination and perceived social support as mediating variables to explore the influence of negative life events on depression. Materials and methods: Due to the COVID-19 pandemic and social distancing, a convenient sampling method was adopted to collect information about middle school students in Shandong Province by means of online questionnaire. Adolescent Self-Rating Life Events Check List, Ruminative Responses Scale, Perceived Social Support Scale and Children's Depression Inventory were used. Descriptive statistics and correlation analysis were conducted for four variables of middle school students, including life events, depression, rumination thinking and perceived social support, and the chain mediated effect was tested by using process plug-in. All statistically analysis was conducted by SPSS 23.0. Results: 493 middle school students (16.7000 ± 0.9500 years) including 343 female students (69.6000%) from Shandong Province recruited. Results showed that the total effect between life events and depression was significant (effect = 0.2535, 95%CI: 0.2146, 0.2924). The total indirect effect was significant (effect = 0.1700, 95%CI: 0.1349, 0.2072). The indirect effect was significant (effect = 0.0988, 95%CI: 0.0741, 0.1252) with rumination as the mediating variable. The indirect effect of pathway with perceived social support as the mediating variable was significant (effect = 0.0476, 95%CI: 0.0295, 0.0674). The indirect effect of pathway with rumination and perceived social support as mediating variables was also significant (effect = 0.0236, 95%CI: 0.0147, 0.0339). Conclusion: This study indicated that ruminant thinking and perceived social support had a significant chain mediating effect on adolescents' life events and depression. Life events can not only directly affect depressive emotions, but also indirectly affect depressive emotions by affecting ruminant thinking and perceived social support. The results of this study not only provide new directions for the relationship between life events and depression, but also provide possible approaches for future prevention and intervention of depression in middle school students.

11.
Front Med (Lausanne) ; 9: 914732, 2022.
Article in English | MEDLINE | ID: covidwho-2022766

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) is a severe acute respiratory disease that poses a continuous threat to global public health. Many non-pharmacological interventions (NPIs) have been implemented to control the COVID-19 pandemic since the beginning. The aim of this study was to assess the impact of various NPIs on COVID-19 mortality during pre-vaccination and vaccination periods. Methods: The COVID-19 data used in this study comes from Our World in Data, we used the Oxford Strict Index (OSI) and its five combination interventions as independent variables. The COVID-19 mortality date (MRT) was defined as a date when daily rate of 0.02 COVID-19 deaths per 100,000 population in a country was reached, and the COVID-19 vaccination date (VRT) was defined as people vaccinated reaching 70%. Linear regression and random forest models were used to estimate the impact of various NPI implementation interventions during pre-vaccination and vaccination periods. The performance of models was assessed among others with Shapley Additive Explanations (SHAP) explaining the prediction capability of the model. Results: During the pre-vaccination period, the various NPIs had strong protective effect. When the COVID-19 MRT was reached, for every unit increase in OSI, the cumulative mortality as of June 30, 2020 decreased by 0.71 deaths per 100,000 people. Restrictions in travel (SHAP 1.68) and cancelation of public events and gatherings (1.37) had major reducing effect on COVID-19 mortality, while staying at home (0.26) and school and workplace closure (0.26) had less effect. Post vaccination period, the effects of NPI reduced significantly: cancelation of public events and gatherings (0.25), staying at home (0.22), restrictions in travel (0.14), and school and workplace closure (0.06). Conclusion: Continued efforts are still needed to promote vaccination to build sufficient immunity to COVID-19 in the population. Until herd immunity is achieved, NPI is still important for COVID-19 prevention and control. At the beginning of the COVID-19 pandemic, the stringency of NPI implementation had a significant negative association with COVID-19 mortality; however, this association was no longer significant after the vaccination rate reached 70%. As vaccination progresses, "cancelation of public events and gatherings" become more important for COVID-19 mortality.

12.
Front Immunol ; 13: 954121, 2022.
Article in English | MEDLINE | ID: covidwho-2022737

ABSTRACT

Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive "cytokines storm", main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Animals , COVID-19 Vaccines , Humans , Macaca fascicularis , Proteomics
13.
Med Phys ; 2022 Sep 03.
Article in English | MEDLINE | ID: covidwho-2013686

ABSTRACT

BACKGROUND: Most of existing deep learning research in medical image analysis is focused on networks with stronger performance. These networks have achieved success, while their architectures are complex and even contain massive parameters ranging from thousands to millions in numbers. The nature of high dimension and nonconvex makes it easy to train a suboptimal model through the popular stochastic first-order optimizers, which only use gradient information. PURPOSE: Our purpose is to design an adaptive cubic quasi-Newton optimizer, which could help to escape from suboptimal solution and improve the performance of deep neural networks on four medical image analysis tasks including: detection of COVID-19, COVID-19 lung infection segmentation, liver tumor segmentation, optic disc/cup segmentation. METHODS: In this work, we introduce a novel adaptive cubic quasi-Newton optimizer with high-order moment (termed ACQN-H) for medical image analysis. The optimizer dynamically captures the curvature of the loss function by diagonally approximated Hessian and the norm of difference between previous two estimates, which helps to escape from saddle points more efficiently. In addition, to reduce the variance introduced by the stochastic nature of the problem, ACQN-H hires high-order moment through exponential moving average on iteratively calculated approximated Hessian matrix. Extensive experiments are performed to access the performance of ACQN-H. These include detection of COVID-19 using COVID-Net on dataset COVID-chestxray, which contains 16 565 training samples and 1841 test samples; COVID-19 lung infection segmentation using Inf-Net on COVID-CT, which contains 45, 5, and 5 computer tomography (CT) images for training, validation, and testing, respectively; liver tumor segmentation using ResUNet on LiTS2017, which consists of 50 622 abdominal scan images for training and 26 608 images for testing; optic disc/cup segmentation using MRNet on RIGA, which has 655 color fundus images for training and 95 for testing. The results are compared with commonly used stochastic first-order optimizers such as Adam, SGD, and AdaBound, and recently proposed stochastic quasi-Newton optimizer Apollo. In task detection of COVID-19, we use classification accuracy as the evaluation metric. For the other three medical image segmentation tasks, seven commonly used evaluation metrics are utilized, that is, Dice, structure measure, enhanced-alignment measure (EM), mean absolute error (MAE), intersection over union (IoU), true positive rate (TPR), and true negative rate. RESULTS: Experiments on four tasks show that ACQN-H achieves improvements over other stochastic optimizers: (1) comparing with AdaBound, ACQN-H achieves 0.49%, 0.11%, and 0.70% higher accuracy on the COVID-chestxray dataset using network COVID-Net with VGG16, ResNet50 and DenseNet121 as backbones, respectively; (2) ACQN-H has the best scores in terms of evaluation metrics Dice, TPR, EM, and MAE on COVID-CT dataset using network Inf-Net. Particularly, ACQN-H achieves 1.0% better Dice as compared to Apollo; (3) ACQN-H achieves the best results on LiTS2017 dataset using network ResUNet, and outperforms Adam in terms of Dice by 2.3%; (4) ACQN-H improves the performance of network MRNet on RIGA dataset, and achieves 0.5% and 1.0% better scores on cup segmentation for Dice and IoU, respectively, compared with SGD. We also present fivefold validation results of four tasks. It can be found that the results on detection of COVID-19, liver tumor segmentation and optic disc/cup segmentation can achieve high performance with low variance. For COVID-19 lung infection segmentation, the variance on test set is much larger than on validation set, which may due to small size of dataset. CONCLUSIONS: The proposed optimizer ACQN-H has been validated on four medical image analysis tasks including: detection of COVID-19 using COVID-Net on COVID-chestxray, COVID-19 lung infection segmentation using Inf-Net on COVID-CT, liver tumor segmentation using ResUNet on LiTS2017, optic disc/cup segmentation using MRNet on RIGA. Experiments show that ACQN-H can achieve some performance improvement. Moreover, the work is expected to boost the performance of existing deep learning networks in medical image analysis.

14.
Medicine (Baltimore) ; 101(31): e29931, 2022 Aug 05.
Article in English | MEDLINE | ID: covidwho-2008660

ABSTRACT

BACKGROUND: The diagnosis and treatment rate of Parkinson disease (PD) with depression has a low diagnostic rate, and there is no consensus on the choice of treatment mode. This study evaluates the global research trends of scientific outputs related to depression in PD from multiple perspectives, using a bibliometric analysis and visualization tool to scientifically analyze the knowledge from the literature. METHODS: Literature related to depression in PD published from 2012 to 2021 was included and selected from the Web of Science Core Collection database in October 2021. CiteSpace software was used to visualize and analyze co-occurrence analyses for countries, institutions, authors, and keywords. RESULTS: A total of 4533 articles from the Web of Science database were included. The United States made the largest contribution with the majority of publications (1215; 29.40%). Toronto University was the most productive institution. PD, depression, quality of life, dementia, nonmotor symptom, prevalence, anxiety, Alzheimer disease, symptom, and disorder would be significantly correlated with depression in PD. The current hot spots in this field focus on the following: risk factors for depression in PD, assessment scale of depression in PD, and rehabilitation of depression in PD. CONCLUSIONS: This analysis not only reveals the current research trends and hotspots but also provides some instructive suggestions on the development of depression in PD.


Subject(s)
Parkinson Disease , Bibliometrics , Depression/epidemiology , Depression/etiology , Humans , Parkinson Disease/complications , Parkinson Disease/epidemiology , Parkinson Disease/therapy , Publications , Quality of Life , United States
15.
Cell Discov ; 8(1): 86, 2022 Sep 06.
Article in English | MEDLINE | ID: covidwho-2008267

ABSTRACT

The ongoing COVID-19 pandemic has continued to affect millions of lives worldwide, leading to the urgent need for novel therapeutic strategies. G-quadruplexes (G4s) have been demonstrated to regulate life cycle of multiple viruses. Here, we identify several highly conservative and stable G4s in SARS-CoV-2 and clarify their dual-function of inhibition of the viral replication and translation processes. Furthermore, the cationic porphyrin compound 5,10,15,20-tetrakis-(N-methyl-4-pyridyl)porphine (TMPyP4) targeting SARS-CoV-2 G4s shows excellent antiviral activity, while its N-methyl-2-pyridyl positional isomer TMPyP2 with low affinity for G4 has no effects on SARS-CoV-2 infection, suggesting that the antiviral activity of TMPyP4 attributes to targeting SARS-CoV-2 G4s. In the Syrian hamster and transgenic mouse models of SARS-CoV-2 infection, administration of TMPyP4 at nontoxic doses significantly suppresses SARS-CoV-2 infection, resulting in reduced viral loads and lung lesions. Worth to note, the anti-COVID-19 activity of TMPyP4 is more potent than remdesivir evidenced by both in vitro and in vivo studies. Our findings highlight SARS-CoV-2 G4s as a novel druggable target and the compelling potential of TMPyP4 for COVID-19 therapy. Different from the existing anti-SARS-CoV-2 therapeutic strategies, our work provides another alternative therapeutic tactic for SARS-CoV-2 infection focusing on targeting the secondary structures within SARS-CoV-2 genome, and would open a new avenue for design and synthesis of drug candidates with high selectivity toward the new targets.

16.
European journal of radiology open ; 9:100438-100438, 2022.
Article in English | EuropePMC | ID: covidwho-1998887

ABSTRACT

Objectives When diagnosing Coronavirus disease 2019(COVID‐19), radiologists cannot make an accurate judgments because the image characteristics of COVID‐19 and other pneumonia are similar. As machine learning advances, artificial intelligence(AI) models show promise in diagnosing COVID-19 and other pneumonias. We performed a systematic review and meta-analysis to assess the diagnostic accuracy and methodological quality of the models. Methods We searched PubMed, Cochrane Library, Web of Science, and Embase, preprints from medRxiv and bioRxiv to locate studies published before December 2021, with no language restrictions. And a quality assessment (QUADAS-2), Radiomics Quality Score (RQS) tools and CLAIM checklist were used to assess the quality of each study. We used random-effects models to calculate pooled sensitivity and specificity, I2 values to assess heterogeneity, and Deeks' test to assess publication bias. Results We screened 32 studies from the 2001 retrieved articles for inclusion in the meta-analysis. We included 6737 participants in the test or validation group. The meta-analysis revealed that AI models based on chest imaging distinguishes COVID-19 from other pneumonias: pooled area under the curve (AUC) 0.96 (95 % CI, 0.94–0.98), sensitivity 0.92 (95 % CI, 0.88–0.94), pooled specificity 0.91 (95 % CI, 0.87–0.93). The average RQS score of 13 studies using radiomics was 7.8, accounting for 22 % of the total score. The 19 studies using deep learning methods had an average CLAIM score of 20, slightly less than half (48.24 %) the ideal score of 42.00. Conclusions The AI model for chest imaging could well diagnose COVID-19 and other pneumonias. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the quality of research methodology and further improve the generalizability of the developed predictive models.

17.
Sensors (Basel) ; 22(15)2022 Aug 08.
Article in English | MEDLINE | ID: covidwho-1994141

ABSTRACT

The development of MEMS acoustic resonators meets the increasing demand for in situ detection with a higher performance and smaller size. In this paper, a lithium niobate film-based S1 mode Lamb wave resonator (HF-LWR) for high-sensitivity gravimetric biosensing is proposed. The fabricated resonators, based on a 400-nm X-cut lithium niobate film, showed a resonance frequency over 8 GHz. Moreover, a PMMA layer was used as the mass-sensing layer, to study the performance of the biosensors based on HF-LWRs. Through optimizing the thickness of the lithium niobate film and the electrode configuration, the mass sensitivity of the biosensor could reach up to 74,000 Hz/(ng/cm2), and the maximum value of figure of merit (FOM) was 5.52 × 107, which shows great potential for pushing the performance boundaries of gravimetric-sensitive acoustic biosensors.


Subject(s)
Acoustics , Biosensing Techniques , Electrodes , Equipment Design , Vibration
18.
J Healthc Inform Res ; 5(3): 231-248, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1270402

ABSTRACT

Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in the absence of any intervention policies. In addition, these models assume full observability of disease cases and do not account for under-reporting. We present a mathematical model, namely PolSIRD, which accounts for the under-reporting by introducing an observation mechanism. It also captures the effects of intervention policies on the disease spread parameters by leveraging intervention policy data along with the reported disease cases. Furthermore, we allow our recurrent model to learn the initial hidden state of all compartments end-to-end along with other parameters via gradient-based training. We apply our model to the spread of the recent global outbreak of COVID-19 in the USA, where our model outperforms the methods employed by the CDC in predicting the spread. We also provide counterfactual simulations from our model to analyze the effect of lifting the intervention policies prematurely and our model correctly predicts the second wave of the epidemic.

19.
J Virol ; 96(16): e0077522, 2022 08 24.
Article in English | MEDLINE | ID: covidwho-1973793

ABSTRACT

Emerging severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, especially the Omicron variant, have impaired the efficacy of existing vaccines and most therapeutic antibodies, highlighting the need for additional antibody-based tools that can efficiently neutralize emerging SARS-CoV-2 variants. The use of a "single" agent to simultaneously target multiple distinct epitopes on the spike is desirable in overcoming the neutralizing escape of SARS-CoV-2 variants. Herein, we generated a human-derived IgG-like bispecific antibody (bsAb), Bi-Nab35B5-47D10, which successfully retained parental specificity and simultaneously bound to the two distinct epitopes on receptor-binding domain (RBD) and S2. Bi-Nab35B5-47D10 showed improved spike binding breadth among wild-type (WT) SARS-CoV-2, variants of concern (VOCs), and variants being monitored (VBMs) compared with its parental monoclonal antibodies (MAbs). Furthermore, pseudotyped virus neutralization demonstrated that Bi-Nab35B5-47D10 can efficiently neutralize VBMs, including Alpha (B.1.1.7), Beta (B.1.351), and Kappa (B.1.617.1), as well as VOCs, including Delta (B.1.617.2), Omicron BA.1, and Omicron BA.2. Crucially, Bi-Nab35B5-47D10 substantially improved neutralizing activity against Omicron BA.1 (IC50 = 0.15 nM) and Omicron BA.2 (IC50 = 0.67 nM) compared with its parental MAbs. Therefore, Bi-Nab35B5-47D10 represents a potential effective countermeasure against SARS-CoV-2 Omicron and other variants of concern. IMPORTANCE The new, highly contagious SARS-CoV-2 Omicron variant caused substantial breakthrough infections and has become the dominant strain in countries across the world. Omicron variants usually bear high mutations in the spike protein and exhibit considerable escape of most potent neutralization monoclonal antibodies and reduced efficacy of current COVID-19 vaccines. The development of neutralizing antibodies with potent efficacy against the Omicron variant is still an urgent priority. Here, we generated a bsAb, Bi-Nab35B5-47D10, which simultaneously targets SARS-CoV-2 RBD and S2 and improves the neutralizing potency and breadth against SARS-CoV-2 WT and the tested variants compared with their parental antibodies. Notably, Bi-Nab35B5-47D10 has more potent neutralizing activity against the VOC Omicron pseudotyped virus. Therefore, Bi-Nab35B5-47D10 is a feasible and potentially effective strategy by which to treat and prevent COVID-19.


Subject(s)
Antibodies, Bispecific , Spike Glycoprotein, Coronavirus/antagonists & inhibitors , Antibodies, Bispecific/metabolism , Antibodies, Monoclonal , Antibodies, Neutralizing , Antibodies, Viral , COVID-19/drug therapy , Epitopes , Humans , Neutralization Tests , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry
20.
Frontiers in immunology ; 13, 2022.
Article in English | EuropePMC | ID: covidwho-1958481

ABSTRACT

Although tremendous effort has been exerted to elucidate the pathogenesis of severe COVID-19 cases, the detailed mechanism of moderate cases, which accounts for 90% of all patients, remains unclear yet, partly limited by lacking the biopsy tissues. Here, we established the COVID-19 infection model in cynomolgus macaques (CMs), monitored the clinical and pathological features, and analyzed underlying pathogenic mechanisms at early infection stage by performing proteomic and metabolomic profiling of lung tissues and sera samples from COVID-19 CMs models. Our data demonstrated that innate immune response, neutrophile and platelet activation were mainly dysregulated in COVID-19 CMs. The symptom of neutrophilia, lymphopenia and massive “cytokines storm”, main features of severe COVID-19 patients, were greatly weakened in most of the challenged CMs, which are more semblable as moderate patients. Thus, COVID-19 model in CMs is rational to understand the pathogenesis of moderate COVID-19 and may be a candidate model to assess the safety and efficacy of therapeutics and vaccines against SARS-CoV-2 infection.

SELECTION OF CITATIONS
SEARCH DETAIL